1. Field of the Invention
This invention relates generally to digital image technology and more particularly to a method and apparatus for partitioning an image into homogeneous regions.
2. Description of the Related Art
Automatic image segmentation is one of the most challenging problems in computer vision. The objective of image segmentation is to partition an image into homogeneous regions. Depth of Field (DOF) refers to the distance from the nearest to the furthest point of perceived “sharp” focus in a picture. Low DOF is a photographic technique commonly used to assist in understanding depth information within a 2 dimensional photograph. Low DOF generally refers to a condition when an object of interest (OOI) is in sharp focus and the background objects are blurred to out of focus. FIGS. 1A through 1C are exemplary illustrations of low DOF images. The butterfly of FIG. 1A is highly focused, i.e., the object of interest, while the background is defocused. The soccer player and soccer ball of FIG. 1B are the objects of interest, since each is highly focused, while the background is defocused. Similarly, with reference to FIG. 1C, the bird is highly focused while the remainder of the image is defocused. Segmentation of images with low DOF is applicable to numerous applications, e.g., image indexing for content-based retrieval, object-based image compression, video object extraction, 3D microscopic image analysis, and range segmentation for depth estimation.
Assuming sharply focused regions contain adequate high frequency components, it should be possible to distinguish the focused regions from the low DOF image by comparing the amount of the high frequency content. There are two approaches for the segmentation of the low DOF images: edge-based and region-based approaches. The edge-based method extracts the boundary of the object by measuring the amount of defocus at each edge pixel. The edge-based algorithm has demonstrated accuracy for segmenting man-made objects and objects with clear boundary edges. However, this approach often fails to detect boundary edges of the natural object, yielding disconnected boundaries.
On the other hand, the region-based segmentation algorithms rely on the detection of the high frequency areas in the image. Here, a reasonable starting point is to measure the degree of focus of each pixel by computing the high frequency components. To this end, several methods have been used, such as spatial summation of the squared anti-Gaussian (SSAG) function, variance of wavelet coefficients in the high frequency bands, a multi-scale statistical description of high frequency wavelet coefficients, and local variance, etc. Exploiting high frequency components alone often results in errors both in focused and defocused regions. In defocused regions, despite blurring due to the defocusing, there could be busy texture regions in which high frequency components are still strong enough. These regions are prone to be misclassified as focused regions. Conversely, focused regions with nearly constant gray levels may also generate errors in these regions. Thus, relying only on the sharp detail of the OOI can be a limitation for the region-based DOF image segmentation approach. Furthermore, the multi-scale approaches tend to generate jerky boundaries even though refinement algorithms for high resolution classification are incorporated.
FIG. 2 is a schematic diagram of the optical geometry of a typical image capture device such as a camera. Lens 100 has the disadvantage that it only brings to focus light from points at a distance-z given by the familiar lens equation:
                                                        1                              z                ′                                      +                          1                              -                z                                              =                      1            f                          ,                            (        2        )            where z′ is the distance of image plane 102 from lens 100 and f is the focal length. Points at other distances are imaged as little circles. The size of the blur circle can be determined as follows: A point at distance − z is imaged at a point z′ from the lens, where 1/ z′+1/− z=1/f, and so
                              (                                                    z                _                            ′                        -                          z              ′                                )                =                              f                          (                                                z                  _                                +                f                            )                                ⁢                      f                          (                              z                +                f                            )                                ⁢                                    (                                                z                  _                                -                z                            )                        .                                              (        3        )            
If image plane 102 is situated to receive correctly focused images of object at distance −z, then points at distance − z will give rise to blur circles of diameter
            d              z        ′              ⁢                                              z            _                    ′                -                  z          ′                            ,where d represents the diameter of lens 100. The depth of field (DOF) is the range of distances over which objects are focused “sufficiently well,” in the sense that the diameter of the blur circle is less than the resolution of the imaging device. The DOF depends, of course, on what sensor is used, but in any case it is clear that the larger the lens aperture, the less the DOF. Of course, errors in focusing become more serious when a large aperture is employed. As shown in FIG. 2, df 104 and dr 106 represent the front and rear limits, respectively, of the “depth of field.” With low DOF, the diameter of blur circle becomes small, thus only the OOI is in sharp focus, whereas objects in background are blurred to out of focus. Additionally, segmentation techniques based upon color and intensity information suffer from poor extraction results.
As a result, there is a need to solve the problems of the prior art to provide a method and apparatus for segmenting an image associated with a low depth of field such that the object of interest may be extracted from the background accurately and efficiently.